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Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas

1,2, 1,*, 1,2 and 1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Author to whom correspondence should be addressed.
Water 2019, 11(4), 786;
Received: 9 January 2019 / Revised: 30 March 2019 / Accepted: 10 April 2019 / Published: 16 April 2019
(This article belongs to the Section Hydrology)
PDF [24572 KB, uploaded 16 April 2019]
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Unsupervised flood detection in large areas using Synthetic Aperture Radar (SAR) data always faces the challenge of automatic thresholding, because the histograms of large-scale images are unimodal, which thus makes it difficult to determine the threshold. In this paper, an iteratively multi-scale chessboard segmentation-based tiles selection method is introduced. This method includes a robust search procedure for tiles which obey bimodal Gaussian distribution, and a non-parametric histogram-based thresholding algorithm for thresholds identifying water areas. Then, the thresholds are integrated into the region-growing algorithm to obtain a consistent flood map. In addition, a classification refinement technique using multiresolution segmentation is proposed to address the omission in a heterogeneous flood area caused by water surface roughening due to weather factors (e.g., wind or rain). Experiments on the flooded area of Jialing River on July 2018 using Sentinel-1 images show a high classification accuracy of 99.05% through the validation of Landsat-8 data, indicating the validity of the proposed method. View Full-Text
Keywords: synthetic aperture radar (SAR); flood detection; bimodality test; target region search; region-growing synthetic aperture radar (SAR); flood detection; bimodality test; target region search; region-growing

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Cao, H.; Zhang, H.; Wang, C.; Zhang, B. Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas. Water 2019, 11, 786.

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